There are various approaches available for optimizing drilling performance. However, many of these schemes, particularly those relying on calculation of gradients to locate an optimum set of control parameters, are unsuitable for wide application without prior knowledge of drilling conditions or are susceptible to errors inherent in drilling performance measurements. Further, existing methods can be confounded by changes, especially unrecognized changes, in formation or drilling conditions. A general issue with these schemes is that the more data points that are collected and used for analysis, the more vulnerable the optimization is to errors due to drilling performance measurement or changes in the formation or drilling conditions. These errors would lead to a false optimum set of control parameters and drilling underperformance. Thus, there is a need for a robust and efficient method of finding an optimum set of control parameters without previous knowledge of drilling conditions and subject to changes in formation and drilling conditions, including changes that are not explicitly recognized.